Deep object segmentation and classification networks for building damage detection using the xBD dataset

Zhao, Zongze; Wang, Fenglei; Chen, Shiyu; Wang, Hongtao; Cheng, Gang

doi:10.1080/17538947.2024.2302577

Cited by 4 publications

(2 citation statements)

References 48 publications

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“…Binary features (7,12,14,20,21,22) describe attributes with two states, while categorical features (2, 3, 4, 42) reflect qualitative variables with distinct categories. Discrete features (8,9,15,(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)43) are unique numeric variables, but continuous features (1,5,6,10,11,13,16,17,18,19) can take any real value within a range. The dataset's 'attack' label has 40 labels, categorizing attacks as revised, U2R, DoS, R2L, and probing.…”

Section: Datasetmentioning

confidence: 99%

“…Moreover, the ECA mechanism is becoming increasingly popular in deep CNNs, especially in scenarios where there is a need for lightweight models that can deliver high performance. ECA surpasses conventional attention modules such as SE [41] and CBAM [41] by disregarding the necessity for dimensionality reduction and emphasizing local cross-channel interactions. Within the framework of an intrusion detection system that utilizes the NSL-KDD dataset, including ECA is proven to be an indispensable element.…”

Section: Utilization Of the Nsl-kdd Dataset For Intrusion Detectionmentioning

confidence: 99%

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CNN Channel Attention Intrusion Detection System Using NSL-KDD Dataset

Alrayes,

Zakariah,

Amin

et al. 2024

CMC

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Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highlyadaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label's "attack" and "level." It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection strategy. Furthermore, maintaining operating efficiency while improving detection accuracy is the primary goal of this work. Moreover, typical NIDS examines both risky and typical behavior using a variety of techniques. On the NSL-KDD dataset, our CNN-based approach achieves an astounding 99.728% accuracy rate when paired with channel attention. Compared to previous approaches such as ensemble learning, CNN, RBM (Boltzmann machine), ANN, hybrid auto-encoders with CNN, MCNN, and ANN, and adaptive algorithms, our solution significantly improves intrusion detection performance. Moreover, the results highlight the effectiveness of our suggested method in improving intrusion detection precision, signifying a noteworthy advancement in this field. Subsequent efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

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